Background: Optimizing patient flow can be one of the greatest challenges for nurse leaders in tertiary referral hospitals. It is essential to implement best processes to enhance the patient flow, to maximize bed availability and avoid boarding. Purpose: To evaluate the impact of PACU boarding on the health outcomes of critically ill stable older postoperative patients waiting for an ICU bed. Methods: This was a case-control study that used secondary data from the Project IMPACT Database populated between 2002 and 2010 in a tertiary referral safety net institution. A total of 145 patients age 65 to 85 years of age who were boarded in the PACU (> 6 hours) were matched 1:1 on admission year and gender to patients who were recovered (< 6 hours [control group]) in the PACU (total sample 290 patients, 145 pairs). Results: The average age of patients was 73 years, 53% were male, and were predominately White (71%). Hospital LOS (20.26 days + 37.2 days) was longer for boarded PACU patients than the control group (14.72 days + 13.55 days) which was clinically relevant (5.5 days) but not statistically significant (p = 0.054). As hypothesized, there was no statistically significant difference between those who boarded in the PACU or recovered in the PACU in terms of postoperative LOS, decline in functional status and hospital mortality. However, postoperative LOS was also clinically relevant with PACU boarders spending on average 4.5 days longer in the hospital than the control group (17.10 + 34.64 versus 12.60 + 12.47 respectively). When controlling for age and race with a postoperative LOS > 9 days, PACU boarders were 1.7 times (95% CI: 1.054-2.767, p = 0.03) the odds of having a longer postoperative LOS than the control group. Conclusions: Further research is needed to identify contributing factors associated with prolonged postoperative LOS in those critically ill older stable surgical patients who board in the PACU after surgery. Nurse leaders responsible for patient flow may also need to consider the potential financial implications of boarding along with quality of care metrics.

Dr.N.P., Nursing Practice  – Drexel University, 2016

Background: Developing and maintaining healthy work environments in healthcare organizations is vital to quality outcomes and optimized reimbursement. While the American Association of Critical Care Nurses’ Standards for Establishing and Sustaining Healthy Work Environments are well established in the acute care setting, little is known about their impact in the post-acute environment. Methods: A modified version of the Healthy Work Environment Assessment Tool (HWEAT) was distributed electronically to nurses, unlicensed assistive personnel, and nurse leaders over a 30-day period. Descriptive statistics were used to analyze demographics and survey responses by item and in the aggregate. Results: The response rate was low, rendering any inferential analysis moot. Participants somewhat-to-strongly agreed in greater than 80% of the responses that the site upholds the Standards in skilled communication, effective decision making, and authentic leadership. Conversely, participants somewhat-to-strongly disagreed in 20% or greater of the responses that the site could improve Standards performance in true collaboration, appropriate staffing, and meaningful recognition. Discussion: Recommendations are made for further exploration of specific aspects of the Standards as well as to repeat the survey. Major limitations of the study include the modification of the HWEAT and a low response rate. Conclusion: Further exploration is necessary to provide trustworthy data for site-specific quality improvement. Further research would better describe how healthy work environment standards may benefit post-acute settings overall.

D.N.P., Nursing Practice  – Drexel University, 2018

Background: According to the Centers for Disease Control, heart failure remained the leading cause of death in 2010. Fifty percent of those diagnosed with HF will die within five years of diagnosis. Over 39 billion is spent on HF annually, creating a huge personal and healthcare expenditure burden. Purpose: To determine if an inpatient multidisciplinary HF program decreases the Medicare 30-day hospital readmission rate for a hospital in Gwinnett County, Georgia. Methods: This quality improvement project utilized a retrospective design. The impact of a multidisciplinary heart failure program on 30-day readmissions was evaluated by comparing HF readmissions during a 6-month period before and after implementation of the program. Inclusion criteria included using data Results: A total of 696 medical records were reviewed for this quality improvement project.Three hundred twenty-one during the pre-intervention period and 375 during the post intervention period. Twenty-seven were eliminated during the pre-intervention period and 28 during the post intervention period due to exclusion criteria. The readmission rate was 12.6% pre-intervention and 16.7% post-intervention. The difference in readmission rates was not significant. Conclusion and recommendations: The multidisciplinary heart failure program did not decrease the Medicare 30-day hospital readmission rate, but there are many factors that may have impacted the readmissions. Since the hospital system plans to continue the program, it may be beneficial to reevaluate the program next year. The addition of a HF outpatient clinic for access to early follow-up care post hospitalization and access to triage care may also be advantageous.

D.N.P., Nursing Practice  – Drexel University, 2016

Background and Objectives: Patients with heart failure (HF) have a high hospital readmission rate within 30 days of discharge due to dyspnea caused by water weight gain. Additionally, 22% of HF patients have cognitive impairment (CI) impacting their ability to engage in self-care activities. There is a paucity of research to measure the impact of cognitive function on change in weight over time. Design: This was an observational, longitudinal study of adult patients, with Class II-IV HF who were evaluated at time of discharge (T1) from a hospitalization for acute exacerbation of HF and at a clinic visit (T2) 4-8 weeks after discharge. The participants completed a 30-point Montreal Cognitive Assessment (MoCA) Inventory tool, and were weighed at T1 and T2. Results: Twenty-one participants (mean age 67.1 ± 12.2, 76.2% male, 81% Caucasian) completed both visits. The MoCA cutoff for mild CI was 24, where 42.9% of this sample scored below 24 at baseline. The change in weight between T1 and T2 was not statistically different (M change 0.43, SD 9.64; 95% CI, - 4.08 to 4.94; p = .84). The Independent T test of a change in weight in those HF participants with and without CI was not statistically different (high MoCA score, M change in weight = 0.62, SD 11.22; low MOCA scores, M change in weight = 0.20, SD 7.94; t(df 18) = - 0.09, p = .93). The re-hospitalization rate was 23.80% (n = 5). No cognitive assessment was documented in the medical record by healthcare professionals at time of hospital admission or at the clinic visit. Conclusion: Findings demonstrated there was no statistically significant difference in change in weight over time when participants were divided into low or high scores on the MoCA. The hospital readmission rate for HF was similar to that reported in other studies. This study found that assessments of cognitive function in HF are not being documented in the medical record. This study lays the groundwork for a larger study of the impact of CI on change in weight over time.

Dr.N.P., Nursing Practice  – Drexel University, 2015

Background: Objective functional screening tools are an important component of the assessment of older adults in the emergency department (ED) setting and are identified as a predictor of adverse events including ED revisits and falls. Gait speed is an easy screening tool that can be performed quickly and safely during the triage process without delaying the care of the patient. A gait speed of <1.0m/s may indicate the need to implement fall precautions, demonstrate an increased risk for lower extremity limitations, hospitalizations, death and an increased assistance with personal care. Objective: The purpose of this quality improvement project was to implement gait speed testing in the ED and to examine the impact of gait speed on the disposition decision outcome. Methods: A prospective descriptive design using a convenience sample of 30 older adult patients presenting to the ED was utilized. Gait speeds were categorized as low (<1.0m/s) and normal (>1.0m/s). Discharge disposition was categorized as discharged, discharged with physical therapy, and discharged with other support services. Data analysis consisted of descriptive statistics and the Fisher’s exact test. Results: The sample was primarily female (n=19 , 63.3%), with a mean age of 71years (SD=8.9). The mean gait speed was .75m/s (SD=.25 Twenty-three patients had low gait speed < 1 m/s. Of those patients with low gait speed, 8 patients (34.8%) were admitted while 15 (65.2%) were discharged home, a result that was not statistically significant (P=1.00, Fisher’s exact test). Conclusion: Gait speed testing could be administered by nursing during the triage process without delaying the patient’s length of stay. In this limited sample, gait speed testing did not impact the use of support services upon discharge. Further staff education is warranted to increase their understanding of the clinical implications of gait speed testing.

D.N.P., Nursing Practice  – Drexel University, 2016

Background and Purpose: In the United States, approximately 5.8 million individuals have heart failure (HF) with newly diagnosed cases annually. Patients diagnosed with symptomatic HF continue to have mortality and readmission rates approximating 5% - 30%, respectively. Palliative care is one transitional care process which may reduce readmission rates from one third to one half. The aim of this quality improvement (QI) project was to evaluate the impact of palliative care (PC) consults on 30-day readmission rates for patients with the primary diagnosis of HF. Methods: A retrospective chart review of HF patients treated at an academic medical center between July 1, 2014 and July 1, 2015 was performed. Data extracted from the medical records included documentation of a PC consult and presence of a 30-day readmission after index HF admission. Demographic and descriptive variables included: sex, age, race, insurance type, and severity of illness. Descriptive statistics and chi-square analysis were conducted to identify the differences in 30-day readmissions by PC consults. Results: A total of 1,032 patient records were retrieved and 222 excluded for not meeting inclusion criteria; the remaining 810 records were used for analysis. The sample was primarily men (59.8%, n = 484), Blacks (46.4%, n = 376), and between the ages of 50 – 64 (44.9%, n = 364). The 30-day readmission rate for the total sample was 16.7%. There was a difference between 30-day readmission rates for those who did not receive a PC consult (15.9%) and those patients who did receive a PC consult (25%). Out of 135 reported 30-day readmissions for HF, 87.4% did not receive a PC consult and 12.6% did receive a PC consult. The results were not statistically significant (p = 0.54). Clinical Implications and Recommendations: Readmission rates were lower in those who received a PC consult however, this was not statistically significant. Further evaluation of PC consults and interventions in patients with HF is warranted.

D.N.P., Nursing Practice  – Drexel University, 2016

Abstract Follow-up Telephone Call Quality Initiative: Do Follow-up Telephone Calls After Discharge Reduce 30-day Hospital Readmission For Transcatheter Aortic Valve Replacement Recipients? Barbara Worgan, MSN, ANP Background: Outcomes of nursing interventions following discharge for recipients of transcatheter aortic valve replacement (TAVR) are scarce. Purpose: This quality improvement project was a retrospective cohort design to evaluate the effectiveness of a follow-up telephone call on reducing 30-day hospital readmission for patients recovering from the TAVR procedure. Methods and Results: A convenience sample of 50 patients was used, 25 who received the follow up phone calls and 25 who did not. Twenty-five patients were selected from those TAVR patients who did received a follow-up telephone call between January 20 – May 20, 2017 and 25 TAVR patients from those who were not exposed to the intervention between January 20 – May 20, 2016. The cohorts were matched based on sex and NYHA CHF classification at time of the hospital admission. Both hospital length of stay and postoperative length of stay were shorter in the intervention group than the control group. Patients who received the follow-up telephone call post discharge spent on average 2.20 days less in the hospital than those who did not receive the follow up telephone call, 3.92 ± 1.9 days vs 6.12 ± 3.95 days respectively, t = 2.53 (34), p = 0.01. Patients who received the follow-up telephone call post-discharge spent on average 1.28 days less in the hospital after their procedure than those who did not receive the follow-up telephone call, 2.84 ± 1.7 days vs 4.12 ± 2.57 days respectively, t = 2.07 (48), p = 0.01. However, there was no significant difference in readmissions within 30 days of discharge in those received the follow-up phone call post-discharge (n=4, 8%) and those who did not receive the follow-up phone call post-discharge (n=4, 8%). Conclusions: The quality improvement follow-up telephone call initiative did not reduce the rate of hospital readmission. Although, this program did not evaluate patient reported outcomes, future projects should not only consider hospital readmissions but other quality measures as well.

D.N.P., Nursing Practice  – Drexel University, 2017